Method and device for analyzing a periodic or semi-periodic signal

ABSTRACT

A device for reducing noise in signals having successive substantially repetitive portions, comprising: an iterative averager operative to superimpose and average said substantially repetitive portions to produce a running average thereof, and an iteration ender comprising a noise analyzer for determining a noise level in said running average and ending operation of said iterative averager when said noise level reaches a predetermined level. Also, a method of obtaining an indication of ischemia in a patient using an ECG signal therefrom, the method comprising: extracting an ECG signal over a duration, extracting from said ECG signal a series of QRS complexes over said duration, extracting high frequency components of said QRS complexes, analyzing said high frequency components over said duration for at least one of a predetermined quality, and inferring from said predetermined quality an indication of ischemia.

FIELD OF THE INVENTION

[0001] The present invention relates to a method and device foranalyzing a periodic or semiperiodic signal and more particularly butnot exclusively to analyzing a signal having the form of a typicalelectrocardiograph signal, again more particularly but not exclusivelyto obtaining an improved signal to noise ratio from such a signal.

BACKGROUND OF THE INVENTION

[0002] The electrocardiograph (ECG) signal describes the electricalactivity of the cardiac muscle as it generates the various stages of theheart wave. Each cycle in the ECG signal may be subdivided into segmentscorresponding to stages of the heart wave, such as the P wave, the QRScomplex, the T wave, the ST segment etc. Thus, the P wave of the ECGsignal is due to depolarization of the atria, the QRS complex todepolarization of the ventricles, and the T wave to repolarization ofthe ventricles. Detection of an altered ECG signal is an importantnon-invasive tool in the diagnosis of cardiac abnormalities. Analysis ofthe ECG signal usually focuses on the ST segment due to its low-noiseand its well-known correlation with cardiac abnormalities such ascoronary artery disease (CAD). As physical stress is known to introducefeatures into the ECG signal indicative of CAD not present in signalsobtained at rest it is common to obtain an ECG signal from a subjectduring a stress test comprising phases of rest, exercise and recoveryfrom exercise.

[0003] In order to obtain sufficient information to provide usefulldiagnostic information from ECG measurements it is thus current practiceto obtain data

[0004] A high level of alignment is a prerequisite for any effectiveaveraging process. Thus, filtering the signal, before performing thealignment, as suggested in the above method, makes it practicallyinapplicable to signals, such as the HF component of ECG, where thesignal to noise ratio in the frequencies of interest is very high.

[0005] Thus the methods known in the art are not effective in theanalysis of signals with a varying level of relatively high noise,especially when transient changes in the signals are of importance. Atypical example of such a signal is the HF ECG component obtained duringthe above-mentioned exercise test, which has been shown to be ofimportance in early detection of ischemia—it has been shown by Beker etal. (Proceedings on computers in Cardiology, IEEE Computer Society,33-35,1992) and Beker et al. (Pacing and Clinical electrophysiology12:2040, 1996) that decrease in the energy level of the HF component ofthe QRS interval during the course of the exercise test may beindicative of ischemia.

[0006] There is therefore a need for a method to enhance thesignal-to-noise ratio in periodic or semi-periodic signals, such as ECGsignals—a method that will eliminate or substantially reduce thedisadvantages of the prior art methods.

SUMMARY OF THE INVENTION

[0007] Embodiments of the present invention provide a process, referredto herein as “adaptive averaging”, for effecting an improvement in thesignal-to-noise ratio of periodic or semi-periodic signals. Preferredembodiments comprise the following features:

[0008] 1. Effective noise reduction, without any a-priori limit on theimprovement of the signal to noise ratio.

[0009] 2. Reduced attenuation of transient changes in the signal, and

[0010] 3. Dynamic response to changes in the noise level of the signal.

[0011] According to a first aspect of the present invention there isthus provided a device for reducing noise in signals having successivesubstantially repetitive portions, comprising:

[0012] an iterative averager operative to superimpose and average saidsubstantially repetitive portions to produce a running average thereof,

[0013] and an iteration ender comprising a noise analyzer fordetermining a noise level in said running average and ending operationof said iterative averager when said noise level reaches a predeterminedlevel.

[0014] Preferably, said iterative averager is operative to take saidsuccessive portions in successive iterative steps.

[0015] Preferably, the device further comprises an aligner for aligningat least some of said substantially repetitive portions one withanother, wherein said signal comprises first frequency and secondfrequency components and said aligner comprises a first frequencycorrelated band pass filter and a second frequency correlated band passfilter to extract respective first and second frequency components,thereby to use said first frequency components to locate an alignmentpoint in successive portions and to use said alignment point to alignsaid second frequency components.

[0016] Preferably, said iteration ender is further operative to end saidoperation of said iterative averager when said repetitive portions areexhausted.

[0017] Preferably, said iteration ender is further operative to end saidoperation of said iterative averager when said running average reaches apreset maximum of included repetitive portions.

[0018] Preferably, the device further comprises a repetitive portionselector for selecting repetitive portions for passing to said iterativeaverager, the repetitive portion selector comprising

[0019] a reference portion store for storing a reference portion,

[0020] a cross correlator for computing a cross correlation between acurrent repetitive portion and said reference portion, and

[0021] a comparator for comparing a result of said cross-correlationwith a predetermined threshold to produce a comparison output,

[0022] and wherein said selector is operable to pass said currentrepetitive portion to said iterative averager in accordance with saidcomparison output.

[0023] Preferably, the device further comprises a reference portiondetermination unit associated with said repetitive portion selector,operable to determine as a reference portion any one of a groupcomprising a first repetitive portion of a current length of saidsignal, a final result of a running average of a previous set ofiterations and a prior determined typical wave.

[0024] Preferably, said reference portion determination unit is operableto dynamically change between members of said group over the course of aset of iterations. Preferably, said reference portion determination unitfurther comprises a reference portion updater for dynamically updatingsaid reference portion during the course of a set of iterations.

[0025] Preferably, the device further comprises a reference portiondeterminer, said reference portion determiner comprising,

[0026] a first store for storing a first set of repetitive portions fromsaid signal,

[0027] a second store for storing a second set of repetitive portionsfrom said signal,

[0028] a cross correlator for cross-correlating repetitive portions fromsaid second set in turn with repetitive portions from said first set toproduce a plurality of cross-correlation results for respectiverepetitive portions in said second set,

[0029] and a reference selector for selecting one of said repetitiveportions in said second set as a reference portion in accordance withits respective cross-correlation results.

[0030] Preferably, said reference selector comprises a threshold levelcomparator for comparing each cross-correlation result with a thresholdand which is operable to select as said reference portion a repetitiveportion having a highest number of respective cross-correlation resultsexceeding said threshold.

[0031] Preferably, the device further comprises a summation unit forsumming cross-correlation results of respective repetitive portions andwhich reference selector is operable to select as a reference portion arepetitive portion having the highest sum of respectivecross-correlation results.

[0032] Preferably, said reference selector comprises:

[0033] a threshold level comparator for comparing each cross-correlationresult with a threshold, and

[0034] a summation unit for summing cross-correlation results ofrespective repetitive portions exceeding said threshold,

[0035] and which reference selector is operable to select as a referenceportion a repetitive portion having a highest sum of respectivecross-correlation results.

[0036] Preferably, the device further comprises a signal extractor forextracting said repetitive portion.

[0037] Preferably, the device further comprises an RMS computation unitfor calculation of the energy level of segments of wave obtained byaveraging a series of said repetitive portions.

[0038] Preferably, the device further comprises an RMS value analysisunit for detecting a falloff in said RMS energy value over succeedingaverages.

[0039] Preferably, the device further comprises a cross-correlation unitfor computing the cross correlation coefficient of an average of aseries of said repetitive portions and a reference wave.

[0040] Preferably, the device further comprises a cross-correlationvalue analysis unit for detecting a falloff in said cross-correlationvalue over succeeding averages.

[0041] Preferably, said aligner further comprises a cross-correlator forcross-correlating a current input with said running average at aplurality of successive alignments and for aligning said signal on thebasis of an alignment giving a maximum cross-correlation.

[0042] Preferably, said aligner further comprises:

[0043] an interpolator for interpolating between said cross-correlationsat said successive alignments to determine a higher accuracy sub-samplealignment, and a wave shifter for shifting said current input inaccordance with said determined sub-sample alignment.

[0044] According to a second aspect of the present invention, there isprovided a device for reducing noise in signals having successivesubstantially repetitive portions, comprising:

[0045] an aligner for aligning at least some of said substantiallyrepetitive portions one with another,

[0046] a repetitive portion selector for selecting repetitive portionsfor passing to said iterative averager on the basis of a comparison witha reference portion, and

[0047] an iterative averager operative to superimpose and average saidaligned, selected portions to produce a running average thereof.

[0048] Preferably, the device further comprises a repetitive portionselector for selecting repetitive portions for passing to said iterativeaverager, the repetitive portion selector comprising

[0049] a reference portion store for storing a reference portion,

[0050] a cross correlator for computing the cross correlation between acurrent repetitive portion and said reference portion, and

[0051] a comparator for comparing a result of said cross-correlationwith a predetermined threshold to produce a comparison output,

[0052] and wherein said selector is operable to pass said currentrepetitive portion to said iterative averager in accordance with saidcomparison output.

[0053] Preferably, the device further comprises a reference portiondetermination unit associated with said repetitive portion selector,operable to determine as a reference portion any one of a groupcomprising a first repetitive portion of a current length of saidsignal, a final result of a running average of a previous set ofiterations and a prior determined typical wave.

[0054] Preferably, said reference portion determination unit is operableto dynamically change between members of said group over a course of aset of iterations.

[0055] Preferably, said reference portion determination unit furthercomprises a reference portion updater for dynamically updating saidreference portion during a course of a set of iterations.

[0056] Preferably, the device further comprises a reference portiondeterminer, said reference portion determiner comprising,

[0057] a first store for storing a first set of repetitive portions fromsaid signal,

[0058] a second store for storing a second set of repetitive portionsfrom said signal,

[0059] a cross-correlator for cross-correlating repetitive portions fromsaid second set in turn with repetitive portions from said first set toproduce a plurality of cross-correlation results for respectiverepetitive portions in said second set,

[0060] and a reference selector for selecting one of said repetitiveportions in said second set as a reference portion in accordance withits respective cross-correlation results.

[0061] Preferably, said reference selector comprises a threshold levelcomparator for comparing each cross-correlation result with a thresholdand which is operable to select as said reference portion a repetitiveportion having a highest number of respective cross-correlation resultsexceeding said threshold.

[0062] Preferably, the device further comprises a summation unit forsumming cross-correlation results of respective repetitive portions andwhich reference selector is operable to select as a reference portion arepetitive portion having a highest sum of respective cross-correlationresults.

[0063] Preferably, said reference selector comprises:

[0064] a threshold level comparator for comparing each cross-correlationresult with a threshold, and

[0065] a summation unit for summing cross-correlation results ofrespective repetitive portions exceeding said threshold,

[0066] and which reference selector is operable to select as a referenceportion a repetitive portion having a highest sum of respectivecross-correlation results.

[0067] According to a third aspect of the present invention there isprovided a waveform frequency component alignment device for aligningfirst frequency components of waveforms having first frequency andsecond frequency components, the device comprising:

[0068] band pass filters for extracting, respective first and secondfrequency components of said waveform,

[0069] a first frequency component aligner for determining a firstfrequency alignment point of a current waveform with another waveformbased on respective first frequency components,

[0070] and a second frequency aligner for aligning said second frequencycomponents of said respective waveforms based on said first frequencyalignment point.

[0071] Preferably, said other waveform is a running average of precedingwaveforms.

[0072] Preferably, said first frequency component aligner furthercomprises a cross-correlator for cross-correlating a current waveformwith said other waveform at a plurality of successive alignments and fordetermining said first frequency alignment point on the basis of a oneof said successive alignments giving a maximum cross-correlation.

[0073] Preferably, said first frequency component aligner furthercomprises an interpolator for interpolating between saidcross-correlations at said successive alignments to determine asub-sample accuracy alignment point between said successive alignments.

[0074] According to a fourth aspect of the present invention there isprovided a device for analyzing high frequency components of ECGsignals, comprising

[0075] a data extractor for extracting said high frequency componentsand

[0076] a data analyzer for determining, from a change over time in atleast a part of said high frequency component, whether said ECG signalcontains an indication of the presence of ischermia.

[0077] Preferably, said at least a part of said ECG signal is a QRScomplex.

[0078] Preferably, said change over time is a fall in the energy levelof succeeding QRS complexes.

[0079] Preferably, said change over time is a fall in across-correlation value of succeeding QRS complexes.

[0080] Preferably, said data extractor comprises a waveform averager forperforming iterative averaging over successive ones of said highfrequency components to obtain a reduced noise version of saidcomponents.

[0081] Preferably, the device further comprises a selector for passingto said waveform averager only those ones of said successive componentswhich exceed a threshold cross-correlation with a reference component.

[0082] According to a fifth aspect of the present invention there isprovided a method for reducing noise in signals having successivesubstantially repetitive portions, comprising:

[0083] superimposing one by one weightwise in iterative steps weightedinstances of at least some of said successive substantially repetitiveportions,

[0084] forming a running average of said portions,

[0085] determining a noise level in said running average, and

[0086] ending said iterative steps when said noise level reaches apredetermined level, thereby to produce an average of said substantiallyrepetitive portions having reduced noise.

[0087] The method preferably further comprises a step of aligning atleast some of said substantially repetitive portions one with another,

[0088] Preferably, said signal comprises first frequency and secondfrequency components and said step of aligning comprises substeps of

[0089] extracting said respective first and second frequency components,

[0090] using said first frequency components to locate an alignmentpoint in each of successive portions, and

[0091] using said alignment point to align said second frequencycomponents of each of said successive portions.

[0092] Preferably, said step of ending said iterative steps furthercomprises ending when said repetitive portions are exhausted.

[0093] Preferably, said step of ending said iterative steps furthercomprises ending when said running average reaches a preset maximum ofincluded repetitive portions.

[0094] The method preferably further comprises the step of selectingrepetitive portions for passing to said iterative averager, the step ofrepetitive portion selecting comprising substeps of:

[0095] storing a reference portion,

[0096] Computing the cross correlation between a current repetitiveportion and said reference portion,

[0097] comparing a result of said cross-correlation with a predeterminedthreshold to produce a comparison output, and

[0098] passing said current repetitive portion for iterative averagingin accordance with said comparison output.

[0099] The method preferably further comprises the step of determiningas a reference portion any one of a group comprising a first repetitiveportion of a current length of said signal, a final result of a runningaverage of a previous set of iterations and a prior determined typicalwave.

[0100] Preferably, said step of selecting comprises the further substepof dynamically changing between members of said group over the course ofa set of iterations.

[0101] Preferably, said step of selecting further comprises dynamicallyupdating said reference portion during the course of a set ofiterations.

[0102] The method preferably further comprises a step of determining areference portion by:

[0103] storing a first set of repetitive portions from said signal,

[0104] storing a second set of repetitive portions from said signal,

[0105] cross-correlating repetitive portions from said second set inturn with repetitive portions from said first set to produce a pluralityof cross-correlation results for respective repetitive portions in saidsecond set, and

[0106] selecting one of said repetitive portions in said second set as areference portion in accordance with its respective cross-correlationresults.

[0107] The method preferably further comprises:

[0108] comparing each cross-correlation result with a threshold, and

[0109] selecting as said reference portion a repetitive portion having ahighest number of respective cross-correlation results exceeding saidthreshold.

[0110] Preferably, said step of determining a reference furthercomprises:

[0111] summing cross-correlation results of respective repetitiveportions and

[0112] selecting as a reference portion a repetitive portion having thehighest sum of respective cross-correlation results.

[0113] Preferably, said step of determining a reference portion furthercomprises:

[0114] comparing each cross-correlation result with a threshold,

[0115] summing cross-correlation results of respective repetitiveportions exceeding said threshold, and

[0116] selecting as a reference portion a repetitive portion having ahighest sum of respective cross-correlation results.

[0117] The method preferably further comprises the step of extractingQRS complexes from an ECG signal to provide said repetitive portion.

[0118] The method preferably further comprises extracting an RMS energyvalue from an average of a series of said repetitive portions.

[0119] The method preferably further comprises a step of analyzing saidRMS energy to detect for the presence of a falloff in said RMS energyvalue over succeeding averages.

[0120] The method preferably further comprises extracting across-correlation value from an average of a series of said repetitiveportions.

[0121] The method preferably further comprises the step of analyzingsucceeding ones of said cross correlation value to detect the presenceof a falloff in said cross-correlation value over succeeding averages.

[0122] Preferably, said alignment step further comprisescross-correlating a current input with said running average at aplurality of successive alignments, and

[0123] aligning said signal on the basis of an alignment giving amaximum cross-correlation.

[0124] The step of alignment preferably further comprises furthercomprising interpolating between said cross-correlations at successivealignments to determine a high accuracy alignment between saidsuccessive alignments.

[0125] According to a sixth aspect of the present invention there isprovided a method for reducing noise in signals having successivesubstantially repetitive portions, comprising:

[0126] aligning at least some of said substantially repetitive portionsone with another,

[0127] selecting repetitive portions for passing to said iterativeaverager on the basis of a comparison with a reference portion, and

[0128] superimposing and averaging said aligned, selected portions toproduce a running average thereof.

[0129] The method preferably further comprises a step of selecting fromsaid repetitive portions for passing to said iterative averager, thestep of selecting comprising substeps of:

[0130] storing a reference portion,

[0131] carrying out a cross correlation between a current repetitiveportion and said reference portion,

[0132] comparing a result of said cross-correlation with a predeterminedthreshold to produce a comparison output, and

[0133] passing said current repetitive portion to said iterativeaverager in accordance with said comparison output.

[0134] The method preferably further comprises the step of selecting asa reference portion any one of a group comprising a first repetitiveportion of a current length of said signal, a final result of a runningaverage of a previous set of iterations and a prior determined typicalwave.

[0135] Preferably, said step of selecting a reference portion includes asubstep of dynamically change between members of said group over thecourse of a set of iterations.

[0136] Preferably, said step of selecting a reference portion includesdynamically updating said reference portion during the course of a setof iterations.

[0137] The method preferably further comprises a step of determining areference point, said step comprising,

[0138] storing a first set of repetitive portions from said signal,

[0139] storing a second set of repetitive portions from said signal,

[0140] cross-correlating repetitive portions from said second set inturn with repetitive portions from said first set to produce a pluralityof cross-correlation results for respective repetitive portions in saidsecond set, and

[0141] selecting one of said repetitive portions in said second set as areference portion in accordance with its respective cross-correlationresults.

[0142] The method preferably further comprises the further steps of:

[0143] comparing each cross-correlation result with a threshold, and

[0144] selecting as said reference portion a repetitive portion having ahighest number of respective cross-correlation results exceeding saidthreshold.

[0145] The method preferably further comprises the further steps of

[0146] summing cross-correlation results of respective repetitiveportions, and

[0147] selecting as a reference portion a repetitive portion having thehighest sum of respective cross-correlation results.

[0148] The method preferably comprises the further steps of:

[0149] comparing each cross-correlation result with a threshold,

[0150] summing cross-correlation results of respective repetitiveportions exceeding said threshold, and

[0151] selecting as a reference portion a repetitive portion having ahighest sum of respective cross-correlation results.

[0152] According to a seventh aspect of the present invention there isprovided a method of aligning waveforms having first and secondfrequency components, said second frequency components being moresubject to noise than said first frequency components, the methodcomprising:

[0153] extracting respective first and second frequency components ofsaid waveform,

[0154] determining an alignment point of a current waveform with anotherwaveform based on respective first frequency components, and

[0155] aligning said second frequency components of said respectivewaveforms based on said alignment point

[0156] Preferably, said other waveform is a running average of precedingwaveforms.

[0157] The method preferably further comprises the further steps of:

[0158] cross-correlating a current waveform with said other waveform ata plurality of successive alignments, and

[0159] determining said alignment point on the basis of a one of saidsuccessive alignments giving a maximum cross-correlation.

[0160] The method preferably comprises the further step of interpolatingbetween said cross-correlations at said successive alignments to obtaina sub sample alignment point.

[0161] According to an eighth aspect of the present invention there isprovided a method for analyzing high frequency components of ECGsignals, comprising the steps of:

[0162] extracting said high frequency components and

[0163] determining, from a change over time in at least a part of saidhigh frequency component, whether said ECG signal contains an indicationof the presence of ischemia.

[0164] Preferably, said at least a part of said ECG signal is at leastpart of a QRS complex.

[0165] Preferably, said change over time is a fall in an RMS energylevel of succeeding QRS complexes.

[0166] Preferably, said change over time is a fall in across-correlation value of succeeding QRS complexes.

[0167] The method preferably further comprises the further step ofperforming iterative averaging over successive ones of said highfrequency components to obtain a reduced noise version of saidcomponents.

[0168] The method preferably further comprises a selection step ofcomparing successive waveforms with a reference component and selectingonly those ones of said successive waveforms which exceed a thresholdcross-correlation level with said reference component for said step ofiterative averaging.

[0169] According to a ninth aspect of the present invention there isprovided a method of obtaining an indication of ischemia in a patientusing an ECG signal therefrom, the method comprising:

[0170] extracting an ECG signal over a duration,

[0171] extracting from said ECG signal a series of at least partial QRScomplexes over said duration,

[0172] extracting high frequency components of said QRS complexes,

[0173] analyzing said high frequency components over said duration forat least one of a predetermined quality, and

[0174] inferring from said predetermined quality an indication ofischemia.

[0175] Preferably, said predetermined quality is a falloff in across-correlation level with a reference component.

[0176] Preferably, said predetermined quality is a falloff in the energylevel of said component.

[0177] Preferably, said step of extracting said high frequencycomponents comprises carrying out iterative steps of averaging overpreselected ones of successive components to reduce noise

[0178] Preferably, said step of extracting an ECG signal is carried outover a duration of a stress test comprising placing the patient in atleast one of a group of phases comprising rest, stress, and recoveryfrom stress.

[0179] Preferably, said step of extracting an ECG signal is carried outover a duration of an event being any one of a group comprising: acutemyocardial ischemia, other forms of heart failure, coronary occlusion,and coronary angioplasty, said duration being any one of a groupcomprising before, during and after said event.

[0180] Preferably, said ECG signal is masked by another ECG signal.

[0181] According to a tenth aspect of the presetninvention there isprovided a method of producing a noise reduced waveform from a series ofsubstantially similar repeated waveforms having superimposed noise, themethod comprising:

[0182] selecting waveforms having a highest cross-correlation with apreselected reference waveform, and

[0183] carrying out iterative averaging steps using said selectedwaveforms.

[0184] The method preferably further comprises the step of ending saiditerative averaging when a signal to noise ratio of a result of saiditerative averaging has a level below a predetermined threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

[0185] For a better understanding of the invention and to show how thesame may be carried into effect, reference will now be made, purely byway of example, to the accompanying drawings.

[0186] With specific reference now to the drawings in detail, it isstressed that the particulars shown are by way of example and forpurposes of illustrative discussion of the preferred embodiments of thepresent invention only, and are presented in the cause of providing whatis believed to be the most useful and readily understood description ofthe principles and conceptual aspects of the invention. In this regard,no attempt is made to show structural details of the invention in moredetail than is necessary for a fundamental understanding of theinvention, the description taken with the drawings making apparent tothose skilled in the art how the several forms of the invention may beembodied in practice. In the accompanying drawings:

[0187]FIG. 1 is a generalized flow diagram showing a method of signalaveraging according to a first embodiment of the present invention,

[0188]FIG. 2 is a generalized flow diagram showing in greater detail thestage of selecting an individual wave by thresholding, according to theembodiment of FIG. 1,

[0189]FIG. 3 is a generalized diagram showing how a newly extracted waveis aligned with an existing set before averaging according to theembodiment of FIG. 1,

[0190]FIG. 4 is a generalized diagram showing a preferred method ofobtaining a reference wave for use in the embodiment of FIG. 1,

[0191]FIG. 5 is a generalized block diagram showing the application ofthe embodiment of FIG. 1 to a system for extracting information from anECG signal,

[0192]FIG. 6 is a generalized flow diagram illustrating an alignmentstage in adaptive averaging of an ECG signal, and

[0193]FIG. 7 is a generalized flow diagram showing noise reduction in awave set extracted from a signal such as an ECG signal.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0194] Before explaining at least one embodiment of the invention indetail, it is to be understood that the invention is not limited in itsapplication to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention is applicable to other embodiments and of beingpracticed or carried out in various ways Also, it is to be understoodthat the phraseology and terminology employed herein is for the purposeof description and should not be regarded as limiting.

[0195] In this specification, terminology is used in accordance with thefollowing definitions:

[0196] 1. “signal” refers to an entire length of periodic orsemi-periodic input

[0197] 2. “segment” indicates an arbitrary part of the signal (usually,but not necessarily, a continuous part of the signal).

[0198] 3. “wave” means a segment consisting of a single occurrence ofthe periodic part of the signal.

[0199] 4. calculable or measurable variables are herein defined to beequivalent to each other if they are substantially proportional to eachother.

[0200] To simplify the following description it is assumed that, unlessexplicitly stated otherwise, the adaptive averaging procedure takes asits input:

[0201] a digitized signal,

[0202] a set of waves extracted from that signal and aligned using anymethod known in the art, and

[0203] for each wave, pointers indicating its correct beginning and endwithin the signal. The skilled person will appreciate that, in order toarrange such inputs, a certain amount of pre-processing using knownsystems will be required. The output of the procedure is an array ofwaves, obtained from the original set of waves by applying noisereduction to the set. Preferably, the output for each waveset is anarray of “cleaned” waves. I.e. whenever the averaged wave resulting fromnoise reduction reaches a satisfactory noise level the algorithm doesnot stop but rather restarts the same procedure, either with the nextwave in the same waveset or with a new waveset, as appropriate in thecircumstances.

[0204] The noise reduction procedure of the preferred embodiments isbased on averaging sets of succeeding waves in an iterative process. Ateach iteration of the adaptive averaging procedure a new wave isincorporated into a set of waves created during the previous stages andan average is calculated over the modified set. As will be describedbelow, an alternative embodiment stores at each iteration only theaveraged wave, and then obtains a new averaged wave using a weightedaveraging wherein, at the n^(th) iteration the averaged wave has weight(n−1) and the new wave has weight 1.

[0205] Unlike the averaging procedure suggested in the above mentionedprior art patent, in which each new wave is averaged with the waveproduced in the preceding step, embodiments of the present invention areable, at least conceptually, to reduce the signal to noise ratio to anydesired level.

[0206] A preferred feature of the present embodiments is that theypermit a user to define a target signal to noise ratio. Such a targetmay be either constant, or may dynamically change according to afunction, supplied by the user, taking as arguments such parameters asthe local noise level, the local signal amplitude etc. The use of aselected target level is advantageous in that it provides assurance thatas soon as the selected target signal to noise ratio has been reached nofurther processing takes place, thus minimizing attenuating effectscaused by surplus noise reduction steps.

[0207] Thus, whenever the required signal to noise ratio level isattained, the current averaged wave is stored in an array of results,and the procedure is repeated with the next wave in the queue. If thetarget level is not reached within a predetermined number of iterations,the procedure stores the result after the predetermined number ofiterations and moves on to the next wave or waveset in a new noisereduction procedure as described above.

[0208] In order to improve the averaging procedure and reduceattenuation in the signal produced by the averaging, waves withexceedingly high noise level are preferably identified so that they arenot incorporated into the average. Therefore, in a preferred embodiment,a reference wave is used in order to rate the waves—for example usingthe cross correlation coefficient between the candidate input wave andthe reference wave—such that only those waves whose rating is above apredetermined level are included in the average.

[0209] Reference is now made to FIG. 1, which is a generalized flowdiagram of a signal averaging procedure according to a first preferredembodiment of the present invention, for enhancing a signal to noiselevel of the signal to a predetermined level. The predetermined levelmay be set as a function of a current noise level in the signal or inany other way. It is desirable that the predetermined level is not tooexacting, as excessive noise reduction tends to harm fine informationstructures in the signal. On the other hand the level should besufficiently high to render the signal intelligible for analysispurposes later on. In FIG. 1, in a first step 10, an input wave set isreceived from an external source and stored in a buffer or other storagemeans. The input wave set may be a segment of a signal and may comprisea plurality of repeated portions (waves). This step is followed by areference definition step 12 in which a reference noise level isdefined, as will be explained in greater detail below, and this isfollowed by a step 14 of taking a next wave of the set obtained in step10. Preferably, the “next wave” is the first wave—in temporal order—notyet incorporated to the running set.

[0210] The next wave is judged against a series of criteria as will bedescribed in more detail below and, if it is found to qualify, is addedto a running set of waves taken from the current input waveset.

[0211] In a step 16, an average is calculated of the running set ofwaves, as updated by the next wave in step 14. In the case of the firstqualifying wave in the set the wave itself constitutes the average.Generally, even though a waveform may vary over time, waves that areclose to one another vary very little and thus averaging such closelyrelated waves is an effective method of removing noise whilst retainingas much as possible of the essential structure of the waveform.

[0212] The step of averaging is repeated by taking succeeding waves inthe waveset, and thus carrying out an iteration of steps 14, and 16until one of the following events occur:

[0213] 1) a reference low noise threshold is reached,

[0214] 2) the input waveset is exhausted of all waves, or

[0215] 3) a predetermined number of iterations has been reached.

[0216] The reference low noise threshold is preferably calculated basedon the reference noise level defined in step 12 above.

[0217] The continuation or ending of the iterative process isillustrated by process step 18, and such control of the iterativeprocess is preferred because it provides a means for ending the noisereduction process before waveform structure is adversely effected. Tothis end the reference noise threshold level and the number ofiterations are preferably both set to optimize for later analysis of thewaveform, which demands minimal noise but maximal surviving waveformstructure.

[0218] At the end of the iteration series of the current waveform set,in step 20, the resulting average waves are stored for later analysis.The original waves that have already been processed from the currentwaveset set and the running waveset may now be deleted if no longerrequired.

[0219] If the current waveset is not exhausted, the running set isdeleted, a next wave is chosen, and the process continues from step 14.The choice of the next wave may be the first wave in the temporallyarranged waveset that has not been processed. Another way of choosingthe next wave may be by choosing, for example the second wave in therunning set. Such a choice preferably ensures that the new average isvery close to the previous one, resulting in a much smoother change inthe array of averaged waves. When the current waveset is exhausted, anew waveset is chosen for iterative analysis, and the that differentwaves having broadly the same waveform may be aligned. Details of theprocess of introducing alignment markers will be discussed in greaterdetail below. In step 42 the next wave is aligned, using the alignmentmarker, with the waves in the running set of extracted waves. Thealigned wave is now added to the set in step 44 and, because all thewaves are aligned it is possible to compute a new average over all thewaves in the running set in step 46.

[0220] Referring back to FIG. 2, a number of possibilities werementioned for obtaining a reference wave and it has been shown how apreferred embodiment uses such a reference wave to decide whether toaccept a next wave into the running set. Reference is now made to FIG. 4which is a generalized flow diagram showing a preferred embodiment forobtaining a reference wave based on individual waves from two wavesets.In FIG. 4 a first waveset is obtained from an input signal andindividual waves are stored in a step 50. Then, in a step 52 a secondset of waves are obtained and stored in the same way. In step 54,successive waves are taken from the second set and in a step 56cross-correlation between each of the waves in the second set and eachof the waves in the first set is computed to produce a cross-correlationresult. Each cross-correlation result is then tested against a thresholdlevel in step 58 and then for each wave in the second set a value k isassigned giving the number of waves in the first set with which therespectively computed cross-correlation coefficient exceeded thethreshold. In step 60 the k value for each individual wave from thesecond set is tested against a threshold, and provided that k exceeds apredetermined threshold, the wave is added to a stored waveset in step62. Otherwise the wave is discarded in step 64.

[0221] The procedure preferably continues until all of the waves in thesecond set have been processed, step 66, and then one of the waves ofthe stored waveset is selected as the reference wave. There are a numberof possibilities for selecting a reference wave from the stored wavesetthat may be considered by the skilled person. One preferred possibilityis to take the wave having the highest sum of cross-correlations andanother preferred possibility is to select the wave having the highest kvalue as a reference value.

[0222] There is thus described, in FIGS. 1 to 4, a system of adaptiveaveraging which allows an input signal comprising a repetitive waveformto be segmented and for a clean version of the waveform to be producedfor later analysis. The system is useful for any input signal having arepetitive portion and unwanted noise and is particularly useful whenanalysis is dependent on careful preservation of fine structuralportions of the signal.

[0223] Reference is now made to FIG. 5, which is a simplified blockdiagram of a system for carrying out adaptive averaging prior toanalysis of an ECG signal. The system comprises a signal acquisitionunit 70, which obtains signals from an input source, in this example, anECG signal.

[0224] The signal is typically an analogue signal containing finestructure, as will be described in further detail below and is generallyinitially mixed with noise. As will be discussed in more detail below,the noise forms a higher proportion of the overall input at frequencybands of interest, such that other frequency parts of the signal aregenerally usable directly, but the signal at the desired frequency partsgenerally requires noise reduction. However, important informationcarried in parts of the signal in the frequency band of interest isliable to be distorted or lost by conventional noise reductiontechniques.

[0225] A signal amplification and digitization unit, 72, processes thesignal so that it can be supplied for digital signal processing. The QRScomplex part of the signal is then detected by a QRS detection unit 74and then an alignment unit 76 detects individual waves and markscorresponding portions of the waves for subsequent alignment.

[0226] The alignment unit is connected in series to noise reduction unit78, HF filtering unit 80 and fine alignment unit 82 respectively, thefunctions of which will be discussed below. Finally a data extractionunit 84 extracts averaged waveforms for subsequent data analysis.

[0227] In order to understand the embodiment of FIG. 5 in greaterdetail, the following preliminary remarks are made regarding analysis ofHF ECG. As has been shown by Abboud et al., an ischemic condition of theheart is highly correlated with significant decrease of the HF ECGsignal of the QRS complex. Further studies by Beker et al. (see above)have shown that a decrease of the HF ECG of the QRS complex duringexercise test could serve as diagnostic aid for early detection ofcardio-vascular infarction, in a way that is much more sensitive thanthe standard ECG signal.

[0228] Enlarging on what was stated above concerning the need for noisereduction, the HF ECG signal is typically weaker and thus harder todetect in a meaningful manner than the standard ECG, and therefore,cannot be usefully dealt with before significantly improving the signalto noise ratio. However, even during a comparatively long test such asan exercise test (typically 10-20 min. long) the decrease in the HF ECGlevel may quite often be observed only in relatively short intervals(2-3 min.). Thus a noise reduction method that requires averaging largenumber of waves can significantly attenuate the effect for which it wasemployed, which means that the decrease in the HF ECG signal is barelynoticeable in the noise reduced signal.

[0229] From the above preliminary remarks it is clear that the analysisof HF ECG preferably requires a noise reduction method that will notabolish transient changes, since the transient changes are of greatdiagnostic significance in that signal. The adaptive averaging method ofthe embodiments described above in respect of FIGS. 1-4 preferablyallows a significant reduction in the noise level of the HF ECG, with aconsiderably weaker attenuation effect on phenomena, particularlytransient phenomena, due to changes in the signal itself. FIG. 5illustrates the general outlines of a specific embodiment intended forthe processing of an HF ECG signal in such a way as to remove noise butto retain transient information.

[0230] The block diagram of FIG. 5 preferably enables a procedure whichpermits analysis of the HF signal of the QRS complex. Thus, in thefollowing may be aligned and then aligning them. In order to perform analignment procedure it is preferable that the complexes are free of anyDC components. Preferably, any such DC components are removed at leastby the time that QRS component detection has been completed, either byrejecting the DC of the raw signal before performing the detection andextraction of the waves, or by performing QRS detection using the rawsignal but extracting the waves themselves from a signal that hasalready been high-pass-filtered.

[0231] It is noted that from a practical point of view, the alignmentunit 76 and the noise reduction unit 78 of the system of FIG. 5 may bemerged and implemented as a single unit, although from the conceptualviewpoint they are distinct. Thus, in the present discussion units 76and 78 will be described separately, although in practice the skilledperson would probably see fit to implement them as a single unitcarrying out both functions together.

[0232] In order to simply the implementation of the alignment unit 76,it is preferable to assume that the waves extracted from the signalaround each maximum point of the cross correlation function sharesubstantially the same length. However, such an assumption is very muchdependent on the circumstances of individual embodiments, in particularon different data extraction methods, and the procedure used by thealignment unit is preferably modified to suit any extraction methodchosen by the user.

[0233] As stated above, the EF ECG signal is relatively weak comparedwith the expected or typical noise level in the signal. Consequently,the cross correlation of a template HF ECG wave with a wave of raw datamay be expected to be dominated by noise, resulting in poor alignmentresults. However, the HF of the QRS complex is considered to correlateeffectively with the standard low frequency wave, i.e. alignment of thelow frequency signal will automatically give an alignment of the HFsignal. As a result, by contrast with the method of U.S. Pat. No.4,732,158, discussed above, alignment is preferably carried out usingthe low frequency wave. The low frequency wave generally hassignificantly better S/N, and thus HF alignment may effectively becarried out on signals with levels of noise that in fact completely maskthe whole HF ECG.

[0234] Before considering the remaining sections of FIG. 5, reference isfirst made to FIG. 6, which is a generalized flow diagram describing apreferred operation of the QRS alignment unit 76 of FIG. 5.

[0235] In the process of FIG. 6, a current set of QRS complexes (waves)is received as input in a first stage 90, preferably together with theraw digitized signal itself and pointers for each complex to show itslocation in the raw digitized signal.

[0236] In a stage 92, a first wave is selected. In a stage 94, areference wave is selected, preferably as has already been described inrespect of FIGS. 1 to 4. Then, in a stage 96, the selected wave isshifted against the reference wave and a series of cross-correlationsare calculated for different relative positions of the two waves. In astage 98 a position corresponding to the maximum cross-correlation isthen determined as a first approximation of the correct alignment of theselected wave. A more accurate location is then preferably obtained in astage 100 by interpolation, as will be described in more detail below.In a step 102, the selected complex is aligned with the reference wavein accordance with the interpolation result of step 100, and this ispreferably followed by a step 104 of updating of the reference wave. Ifthere are more waves (query step 106) then a new wave is loaded as theselected wave (step 108) and cross-correlation/alignment is repeated.

[0237] Particular emphasis in FIG. 6 is laid on the steps choice of andupdating of the reference wave (94 and 104) and the interpolation of thesub-sample location of the alignment or fiducial point (step 100).

[0238] The reference wave is preferably used to define the fiducialpoint with which alignment is preferably made. Each selected wave isaligned, using the fiducial point, with the reference wave, and byassuming transitivity, alignment of the whole set of waves is thusachieved. In order for transitivity to apply, the reference wave ispreferably selected and updated in such a way as to ensure thatalignment responds to changes of the wave with time (such a change mayfor example be indicative of development of a Bundle Branch Block duringthe exercise test) but, on the other hand does not respond to local ortransient changes (such as Premature Ventricular Contractions, shouldthese not have been taken care of earlier by previous processing of thesignal).

[0239] As discussed in detail above, there are many methods for thechoice of the reference wave that may be used in step 94. Among thesemethods the following are preferred:

[0240] 1. The first wave in the current iteration of the process.

[0241] 2. A typical wave obtained during previous measurements orsynthetically created according to theory.

[0242] 3. The method outlined above in respect of FIG. 4, namelyobtaining a set of aligned waves corresponding to a second signal(typically which is similar to the first signal) and executing thefollowing steps:

[0243] a. Store k (a predetermined number) waves from that set.

[0244] b. For each of the stored waves:

[0245] i. Calculate the cross-correlation of each of the k waves with n(a predetermined number) successive waves of the first signal.

[0246] ii. For each of the k waves of the first set determine thenumber, N_(i) (0≦i≦k), of waves out of the n waves of the second setwith which its cross-correlation coefficient (as calculated in step i)exceeds a predetermined threshold.

[0247] iii. From the first set of k waves create a set of “candidates”containing all those waves with N_(i) (the number determined in stageii) above a predetermined value (depending on n).

[0248] C. If the set of “candidates” created in stage iii is empty,reiterate the process with a new set of n waves (as in stage ii).

[0249] d. If the set of candidates created in stage iii is not empty,choose one of the candidates of that set. The choice of the referencewave from the candidate set may be made as follows:

[0250] i. A wave for which the sum of the calculated cross-correlationvalues is maximal; or

[0251] ii. A wave having a maximal number of cross-correlation valuesabove the predetermined threshold.

[0252] For step 104, the update of the reference wave, a few approachesmay be considered, for example:

[0253] 1. Continuously updating the reference wave.

[0254] 2. Updating the reference wave whenever a predetermined number ofwaves have been aligned since the last update.

[0255] 3. Updating the reference wave when the current reference wavegives poor cross correlation coefficients with a predetermined number ofconsecutive waves.

[0256] The update itself may be carried out in numerous ways, including:

[0257] 1. Repeating the procedure outlined above for the choice of areference wave each time an update is required.

[0258] 2. Using a wave obtained by averaging over the last n wavesaligned in the process, where n is a predetermined number.

[0259] Step 100 of the process comprises interpolation of a sub-samplelocation for the fiducial point. Consider, for example, the analysis ofthe 150-300 Hz band of the QRS complex. An A/D converter with a samplingrate of 1 KHz assures that no information in the desired band is lost.However the accuracy of an A/D converter of 1 KHz in the time domain isof 1 ms, which does not permit sufficiently accurate alignment (at 250Hz an error of 1 ms corresponds to a phase shift of 90° ).

[0260] For each wave, the fiducial point, used as the basis foralignment, is the point giving maximum cross correlation of the selectedwave with the reference wave (steps 96 and 98). Thus, obtaining asub-sample accuracy (‘sub-sample accuracy’ meaning accuracy in excess ofthe sampling rate of the A/D converter) for the fiducial point amountsto interpolating the exact location of the maximum of the crosscorrelation function CC(t) defined in step 96. This can be effectivelyachieved as the cross correlation function may be considered a verysmooth function. Thus relatively incomplete data may be analyticallyinterpolated by any method known in the art to give an exact location ofthe maximum beyond the sampling resolution. A simple method for theinterpolation of the exact location of the maximum of thecross-correlation function involves approximating it to a polynomial ofdegree k (depending on the desired accuracy) using the values of CC(t)at, say, 2 k points about the maximum indicated by stage 98. In generalthe result of interpolation stage 100 is given as a fractioncorresponding to an abstract point between two given points of thedigitized signal. Thus, in order to correctly perform the alignment thewhole wave must be shifted by that fraction (i.e. the correct values ofthe signal at intermediate points (n+fraction) has to be calculated).This can be easily achieved (e.g. by using an interpolation filter) asthe sampling rate of the signal is assumed to be adequate.

[0261] The alignment algorithm of FIG. 6 thus preferably results in aset of aligned QRS complexes or waves. The noise reduction stage 78 ofFIG. 5, may thus receive inputs as follows:

[0262] 1. The set of aligned waves.

[0263] 2. The raw digitized signal.

[0264] 3. For each wave: pointers to its location in the signal.

[0265] Reference is now made to FIG. 7, which is a simplified flowdiagram showing in more detail the noise reduction stage 78 of FIG. 5.

[0266] In FIG. 7, a first step 110 indicates receipt of the above listedinputs. In step 112 a first wave is selected for processing. In step114, a reference wave and a reference noise level are selected. In astep 116, the cross-correlation function of the aligned selected wavewith the preselected reference wave (preferably still available from thealignment stage) is compared with a threshold. Provided that the resultexceeds the threshold the selected wave is then added in step 118 to therunning set. If the threshold is not exceeded then the selected wave isdiscarded and the procedure moves on to the next wave. If the selectedwave was added then the running wave set is averaged in step 120 andthen the noise level of the newly averaged wave is compared to areference in a step 122. Preferably, as will be discussed in more detailbelow, the step of discarding waves with low cross-correlation to thereference wave ensures that noise is not added to the running set priorto averaging.

[0267] Then, in decision stages 122, 124 and 126, the process ends thenoise reduction stage if the noise level has dropped below the referencelevel, or if the number of waves averaged has reached a predeterminedthreshold, or there are no more waves left. Otherwise the process isrepeated with the next wave in the set.

[0268] Particular emphasis in FIG. 7 is laid on the steps of thedefinition of the reference wave and the reference noise level (step114) and the termination points of each iteration of the algorithm(steps 122, 124, 126 and 128).

[0269] The choice of the reference wave in the procedure of FIG. 7 maybe made in the same way as in the procedure of FIG. 6, and in animplementation combining the two procedures into one, it may beconvenient to use the same reference wave.

[0270] In the procedure of FIG. 7 the reference wave is preferably usedto identify waves with comparatively high levels of noise for exclusionfrom the running set. Waves whose cross-correlation coefficient with thereference wave is not high enough to satisfy a predetermined thresholdare not incorporated into the average wave in step 118 but are insteaddiscarded, thus reducing the number of waves used in the averagingprocess.

[0271] The reference noise level is needed to calculate the S/N ratio inthe raw signal and to assess an expected amount of averaging needed toreach the target S/N. Alternatively, the reference noise level may beused to determine a target S/N that is deemed sufficient for theanalysis of the signal. The target S/N is preferably a level that may bereached by averaging over a sufficiently small number of waves such thattransient changes, which may be of importance in the subsequentanalysis, will not be attenuated.

[0272] In either of the above cases the reference noise level ispreferably regarded as an input for a function, preferably supplied bythe user, that defines when the S/N level of the averaged signal hasreached a satisfactory level (step 122) and that also defines athreshold on the maximal size of the set of averaged waves (step 124).

[0273] Whichever of the two possibilities is selected for use of thereference noise level as described above, the reference noise level ispreferably calculated from a signal to noise analysis of the raw signalin the following way:

[0274] a. Using the pointers of the waves in the current set to indicatecorrect points of origin of the respective waves in the initial rawsignal, and with access to that signal, segments are preferably selectedover which the reference noise level is to be computed, as segments inthe “neighborhood” of the waves in the set. Alternatively, the noiselevel may be calculated using any segment of the raw signal.

[0275] b. Define a set of “quiet” segments:

[0276] i. If the set of waves does not cover the whole neighborhood ofthe segment to be considered, (that is, in the given segment there areintervals between some of the waves) preferably choose new segments outof these intervals.

[0277] ii. Alternatively, choose a number of waves from the neighborhoodof a segment to be considered. In each wave find an interval duringwhich there is a low signal level, and choose segments for noiseanalysis from these intervals.

[0278] Regardless of the way the “quiet” segments have been chosen, itis preferable to ensure that the total length of the segments selectedfor noise analysis surpasses a predetermined threshold.

[0279] c. Defining a reference noise level based upon the noise levelsof the segments selected in the previous stages. The noise level in eachsegment, can be defined, for example as the root mean square (RMS) ofthe signal in that segment.

[0280] Each wave not eliminated after step 116, is now preferablyincorporated into the running set, that is to say along with allprevious waves of the same set which have not been eliminated. At eachiteration, the average wave of the updated set is preferably calculated(step 120).

[0281] The noise level of the average wave is now calculated in step 122as described above. Step 122 bears close attention as it ensures that assoon as the desired S/N ratio of the average wave (as determined by theuser) has been reached no further averaging of the present set takesplace, thereby reducing attenuating effects due to surplus noisereduction. As long as the required S/N has not been attained thealgorithm keeps adding new waves to the averaged set, until a thresholdnumber of waves (preferably determined according to a function suppliedby the user) is reached. When such a threshold is reached (step 124)iteration on the current waveset is stopped, and a new iteration processstarts with the next waveset (segment etc.) in the queue, as discussedwith regard to the continuation of the process of FIG. 1 after step 20.

[0282] Returning now to FIG. 5, and after the termination of the noisereduction procedure (section 78), preferably the resulting S/N ratio ofthe averaged wave, in the frequency band of interest, is favorable. Now,in a filter unit 80, the averaged waves are preferably band-passfiltered to leave only the frequency band of interest. The design of theband-pass filter for the processed, noised reduced standard ECG QRScomplexes obtained from the previous stages preferably takes intoaccount several parameters including:

[0283] 1. The locality of the phenomenon in the time domain (that is tosay, when looking for a given phenomenon in the QRS complex—either inthe standard ECG or the HF ECG—what is the coarsest time resolution inwhich we may reasonably expect to be able to detect the phenomenon?).

[0284] 2. The locality of the phenomenon in the frequency domain. It isnoted that important phenomena in the HF ECG do not have any trace inthe standard ECG. As the energy levels of the ECG signal in the lowerband of the spectrum are much higher, the locality of a phenomenon inthe frequency domain is defined as the largest frequency range in whichthe low frequency signal will not conceal it.

[0285] Filter parameters selected in accordance with either one of thetwo considerations above may be mutually exclusive. In practice anoptimal filter is selected based on the individual traits of thephenomenon to be dealt with.

[0286] It should be stressed that different implementations of thealgorithm may call for different filters and different frequency bands.These parameters should preferably be selected in accordance with therequirements of the data analysis in the given case being taken intoaccount.

[0287] Although, for the purpose of the alignment procedure it has beenassumed above that the HF signal of the QRS complex is highly correlatedwith the low frequency or standard QRS, local phenomena in the lowfrequency ECG, which have no trace in the HF ECG (e.g. transient“notches” that appear or disappear during exercise) may cause slightdeviations in the alignment of the HF signal. Realignment of thefiltered waves (say as in step 102 in FIG. 6, with the band-passfiltered signals as input) will usually eliminate such deviations.

[0288] Preferably, an additional stage of noise reduction (not appearingin the diagram of FIG. 5) may be applied to the set of filtered andrealigned waves. More precisely, after the first noise reductionprocedure (unit 78) has reduced the S/N ratio in the band of interest toa level that makes it possible to align the band-pass filtered waves, areiteration of the procedure of FIG. 7 may be carried out using thealigned band-pass filtered set of waves as input. Partitioning the noisereduction process into two stages has two main advantages:

[0289] 1. Reiterating the alignment algorithm with the band-passfiltered waves helps exclude certain extremely noisy or abnormal waveswherein the anomaly is not detectable in the standard, or low frequencyECG signal, (it will be recalled that initial alignment was carried outusing the low frequency version of the wave) and

[0290] 2. As stated above, alignment using band-pass filtered wavesgives a better alignment of the HF ECG than that achieved using standardECG, thus reducing the risk of attenuation, during the averagingprocedure, of important data contained in the signal.

[0291] It is noted that, in order to implement the system of FIG. 5using the partitioning of the noise reduction procedure as outlinedabove, the user may preferably supply the procedure of FIG. 7 with twodifferent noise level targets (as in stage 7 of that algorithm) asfollows:

[0292] 1. A noise level that allows an efficient alignment of theband-pass filtered waves. (The “target noise” for the first iteration),and

[0293] 2. The desired final noise level, required for the inspection andanalysis of the signal. (The “target noise” for the second iteration).

[0294] In an alternative embodiment, the fine alignment procedure (usingfine alignment unit 81 in FIG. 5) may be dispensed with. In its place, aprocedure utilizing the following steps is preferably adopted:

[0295] 1. Proceed with signal acquisition, signal amplification anddigitization, QRS complex detection and QRS complex alignment, therebyto create an array of aligned waves complete with pointers to theircorrect original location in the raw signal.

[0296] 2. Use the aligned waves to create an array of waves withrelatively low level of noise in the frequency band of interest. Thiscan be done as described above in respect of the noise reduction unit 78of the system of FIG. 5, or more simply by averaging a predeterminednumber of consecutive waves in the array.

[0297] 3. Band-pass filter each of the waves obtained at the previousstage to the desired frequency range to create a set of reference HFwaves.

[0298] 4. Band-pass filter each of the waves obtained by signalacquisition unit 70 to the desired frequency range to form an array ofHF waves.

[0299] 5. Proceed with the alignment and noise reduction procedures ofunits 76 and 78 in the system of FIG. 5 (as described in FIGS. 6 and 7respectively) using the set of HF waves created in the previous unit asinput and with the array of averaged and band-passed waves frompreceding stages or otherwise obtained as reference waves.

[0300] The above procedure avoids relying on the low frequency signalfor performing alignment, thus bypassing the inaccuracies referred toabove requiring a separate fine alignment procedure.

[0301] Preferably, each segment selected for examination contains a QRScomplex and only one such complex. Thus, the restriction of thealignment procedure to a single wave at a time makes it possible toperform alignment over waves with relatively high S/N ratio.Furthermore, the cross-correlation function of the LF signals (See FIG.6 above) preferably gives a clear indication as to where in each segmentto look for the fiducial point corresponding to that segment.

[0302] However, as the S/N ratio in the frequency band of interest istypically much lower than in the standard low frequency part of the ECGsignal there may be circumstances in which the S/N in the requiredfrequency band will not permit alignment in that band even using theabove-described procedure, whilst in the standard ECG, an acceptablealignment could yet be achieved. Thus in order to implement theabove-described modified procedure some assumption regarding the S/N inthe band of interest is preferably made to give a yardstick to determinewhether or not the alignment according to the modified procedure issufficiently accurate. Generally, in the case of the QRS complex, thepart thereof being of interest and having the higher noise level is ahigh frequency component thereof.

[0303] As mentioned above, analysis of the HF QRS preferably serves as adiagnostic tool for early detection of ischemia. Therefore, various dataextraction procedures (unit 84 of FIG. 5) described in the followingsection focus on the extraction of those parameters currently believedto be diagnostically significant. The skilled person will appreciatethat future developments may add new parameters to the list of thosebelieved to be diagnostically significant and may render some or all ofthose currently so regarded as redundant or otherwise insignificant. Theskilled person will thus appreciate the need to modify the dataextraction unit 84 in the light of such developments.

[0304] In the following, for the sake of simplicity it will be assumedthat data extraction procedures described below have access to thefollowing data:

[0305] 1. An array of aligned noise reduced HF QRS waves.

[0306] 2. An array of aligned standard ECG waves.

[0307] 3. For each wave, pointers to its correct location in the rawsignal.

[0308] 4. The raw signal.

[0309] 5. The band-pass filtered raw signal.

[0310] 6. Any other parameter calculated at each of the previous stageseven if it was not specifically mentioned that it has been stored in anykind of storage device, including, but not limited to, thecross-correlation of each wave with the relevant reference wave, and theS/N ratio in the signal at different stages of the test being carriedout on the patient.

[0311] The following specific embodiments of the data extraction unit 84are taken from experiments which explored the 150-250 Hz frequency band.The embodiments are not, of course, in any way limited to those specificfrequencies.

[0312] As shown by Beker et al, a decrease in the total energy of the HFQRS signal during exercise gives a strong correlation with an ischemiccondition of the heart. In what follows the RMS of an HF QRS signal istaken to represent the energy. A decrease in the total energy of thesignal during exercise can be looked for in any of the following ways:

[0313] 1. Choose an arbitrary point during a rest period and compute theRMS of the HF QRS at that point. Compare that value with the RMS valueof an HEF QRS at an arbitrary point at peak exercise.

[0314] 2. Proceed as in 1 above except that the reference point at restis chosen as the one with minimum RMS value.

[0315] 3. Proceed as in 1 or 2 above where the reference point duringexercise is chosen right after peak exercise.

[0316] 4. Compute the mean RMS value of the HF QRS over the entirety ofa rest stage of the test and compare it to the RMS value at a point oftime during an exercise stage.

[0317] 5. Proceed as in 4 above where a mean RMS calculated for a reststage is compared with a mean RMS calculated for the exercise stage.

[0318] 6. Proceed as in 5 where the mean RMS over the exercise stage istaken over a period of relatively stable heart rate.

[0319] 7. Proceed as in any of 1-6 with any of a set of leads forobtaining ECG signals or any combination of the leads. For example:

[0320] a. The group of leads may include all the precordial leads, allthe frontal leads or any other—less standard partition of the leads, forexample according to those areas of the heart that they cover.

[0321] b. Proceed as in 1-6 for non-conventional ECG leads correspondingto areas of the heart that are not significant enough in the standardECG leads.

[0322] c. Proceed as in a or b where the analyzed signal is thevectorial sum of the signals in the group (i.e. given a group of leads,combining it into a single vector by summing all the leads in thatgroup, taking into account their spatial position).

[0323] 8. Proceed as in any of 1-7 where the RMS is not calculated overthe whole QRS complex but over any predetermined portion of the wave.The size and location of the portion of the wave to be examined may begiven either in absolute values relative to a fiducial point in the wave(e.g. a portion of 30 ms starting 20 ms after the onset of the signal)or relative to the size of each wave (e.g. that 50% of the wave startingat the beginning of its second quarter).

[0324] In a paper by Abboud (Progress in Cardiovascular Diseases VolXXXV. No. 5 March/April 1993) it has been demonstrated that transientischemia in patients undergoing percutaneous transluminal coronaryangioplasty (PTCA) of a critical stenosis in the left anteriordescending (LAD) coronary artery can be detected in the HF QRS wave. Ithas been shown that the inflation of the balloon (and the transientischemia it induces) corresponds to a sensible decrease on a graphdepicting the normalized cross-correlation coefficient of a (constant)template HF QRS and the real signal. Thus, any of the methods ofsections 1-8 of the previous paragraph can be applied using thecross-correlation function in place of the RMS function or in additionthereto.

[0325] Thus, analysis of the HF ECG may serve as a very sensitivenon-invasive diagnosis tool for the detection of ischemia. Furthermore,the methods for HF ECG analysis suggested herein may be designed tosupply on-line results without using hardware additional to standard ECGequipment. Thus, the present embodiments may be readily applied in allsituation where standard ECG is used to monitor the heart's condition.Thus, different specific embodiments of the said methods include, amongothers:

[0326] 1) Screening exercise tests for early detection of ischemia. Themethods suggested herein may serve to gauge the patient's HF ECG signalat rest (i.e. before exercise), the evolution of the signal under stress(i.e between rest and peak exercise) and during a recovery period (i.e.from peak exercise till the heart rate returns to its normal level)carry out a comparison and thus extract diagnostically significant data.The information thus obtained can be compared to the data of previousexercise tests undergone by the same patient, in order to follow theevolution of the cardiac condition over time.

[0327] 2) Monitoring the evolution over time of coronary perfusion inCAD patients undergoing a drug treatment or during a rehabilitationperiod after cardiac surgical interventions. That is, using data ofseveral consecutive HF ECG tests over a relatively short period of time(several days or weeks) it is possible to monitor the improvement in thecoronary perfusion of the patient in order to assess the effectivenessof the treatment.

[0328] 3) ER and surgery room on-line monitoring of patients duringheart failure such as acute myocardial infarction or during PTCA.On-line analysis of the HF ECG in such situations may help the diagnosisin ERs (whether or not the patient suffers from ischemia) and to assessthe immediate improvement of the coronary perfusion during a cardiacsurgical intervention.

[0329] 4) Integration of the embodiments into any (not necessarilycardiac) monitoring systems, including but not in any way limited tostandard ECG monitoring.

[0330] In addition, the various embodiments of the present invention maybe applied to the extraction of a low amplitude signal from inputcontaining a high amplitude signal masking the low amplitude signal.That is, as long as noise is not correlated with the low amplitudesignal that it masks the embodiments of the present invention areapplicable, even if the noise is not evenly distributed in the spectrumdomain. A typical embodiment of the method in such a case is fetal ECGmonitoring, where the signal of interest is masked by the mother's muchstronger signal. In that case the method is applied to detect themother's ECG, and to create therefrom a set of dynamically chainingtemplates that may subsequently be subtracted from the original data,thus to leave only the fetal ECG.

[0331] In accordance with the above described embodiments there are thusprovided embodiments of the present invention which provide in variousaspects:

[0332] 1) The use of the high frequency part of an ECG signal, morespecifically the high frequency components of the QRS complex, in earlydetection of cardio-vascular infarction, tests involving the highfrequency part of the signal being typically more sensitive than thestandard ECG signal. In general, a decrease of the HF ECG of the QRScomplex during stress test serves as an indication of such a condition.

[0333] 2) Alignment of high frequency parts of ECG waves using the lowfrequency parts of the waves.

[0334] 3) Reducing excessive-noise related artifacts by by selectingwaves having highest cross-correlation levels with a reference wave.

[0335] 4) A noise reduction procedure involving successive averaging ofaligned waves, the procedure being modified for minimal distortion of anoverall waveform by selecting a predetermined SNR and terminatingiteration when the predetermined SNR is reached.

[0336] 5) Extraction of an RMS of a high frequency component of a QRScomplex of an ECG signal to determine whether there is a successivedecrease in the level of such an RMS. The presence of such a successivedecrease may be used to indicate the probability of the presence ofischemia.

[0337] 6) Extraction of a cross-correlation function of a high frequencycomponent of a QRS complex of an ECG signal to determine whether thereis a successive decrease in the level of such a function. The presenceof such a successive decrease may be used to indicate the probability ofthe presence of ischemia.

[0338] Generally, the probability of the presence of any of theconditions referred hereinbefore in the human (or animal) body infers asignal given by a machine to draw the attention of medical personnel tothe possibility of the presence of a condition.

[0339] It is appreciated that certain features of the invention, whichare, for clarity, described in the context of separate embodiments, mayalso be provided in combination in a single embodiment. Conversely,various features of the invention which are, for brevity, described inthe context of a single embodiment, may also be provided separately orin any suitable subcombination.

[0340] It will be appreciated by persons skilled in the art that thepresent invention is not limited to what has been particularly shown anddescribed hereinabove. Rather the scope of the present invention isdefined by the appended claims and includes both combinations andsubcombinations of the various features described hereinabove as well asvariations and modifications thereof which would occur to personsskilled in the art upon reading the foregoing description.

1. A device for reducing noise in signals having successive substantially repetitive portions, comprising: an iterative averager operative to superimpose and average said substantially repetitive portions to produce a running average thereof, and an iteration ender comprising a noise analyzer for determining a noise level in said running average and ending operation of said iterative averager when said noise level reaches a predetermined level.
 2. A device for reducing noise in signals according to claim 1, wherein said iterative averager is operative to take said successive portions in successive iterative steps.
 3. A device for reducing noise in signals according to claim 1, further comprising an aligner for aligning at least some of said substantially repetitive portions one with another, wherein said signal comprises first frequency and second frequency components and said aligner comprises a first frequency correlated band pass filter and a second frequency correlated band pass filter to extract respective first and second frequency components, thereby to use said first frequency components to locate an alignment point in successive portions and to use said alignment point to align said second frequency components.
 4. A device for reducing noise in signals according to claim 1, wherein said iteration ender is further operative to end said operation of said iterative averager when said repetitive portions are exhausted.
 5. A device for reducing noise in signals according to claim 1, wherein said iteration ender is further operative to end said operation of said iterative averager when said running average reaches a preset maximum of included repetitive portions.
 6. A device for reducing noise in signals according to claim 1, further comprising a repetitive portion selector for selecting repetitive portions for passing to said iterative averager, the repetitive portion selector comprising a reference portion store for storing a reference portion, a cross correlator for computing a cross correlation between a current repetitive portion and said reference portion, and a comparator for comparing a result of said cross-correlation with a predetermined threshold to produce a comparison output, and wherein said selector is operable to pass said current repetitive portion to said iterative averager in accordance with said comparison output.
 7. A device for reducing noise in signals according to claim 6, further comprising a reference portion determination unit associated with said repetitive portion selector, operable to determine as a reference portion any one of a group comprising a first repetitive portion of a current length of said signal, a final result of a running average of a previous set of iterations and a prior determined typical wave.
 8. A device for reducing noise in signals according to claim 7, wherein said reference portion determination unit is operable to dynamically change between members of said group over the course of a set of iterations.
 9. A device for reducing noise in signals according to claim 7, wherein said reference portion determination unit further comprises a reference portion updater for dynamically updating said reference portion during the course of a set of iterations.
 10. A device for reducing noise in signals according to claim 6, comprising a reference portion determiner, said reference portion determiner comprising, a first store for storing a first set of repetitive portions from said signal, a second store for storing a second set of repetitive portions from said signal, a cross correlator for cross-correlating repetitive portions from said second set in turn with repetitive portions from said first set to produce a plurality of cross-correlation results for respective repetitive portions in said second set, and a reference selector for selecting one of said repetitive portions in said second set as a reference portion in accordance with its respective cross-correlation results.
 11. A device for noise reduction in a signal according to claim 10, wherein said reference selector comprises a threshold level comparator for comparing each cross-correlation result with a threshold and which is operable to select as said reference portion a repetitive portion having a highest number of respective cross-correlation results exceeding said threshold.
 12. A device for noise reduction according to claim 10, wherein said reference selector comprises a summation unit for summing cross-correlation results of respective repetitive portions and which reference selector is operable to select as a reference portion a repetitive portion having the highest sum of respective cross-correlation results.
 13. A device for noise reduction according to claim 10, wherein said reference selector comprises: a threshold level comparator for comparing each cross-correlation result with a threshold, and a summation unit for summing cross-correlation results of respective repetitive portions exceeding said threshold, and which reference selector is operable to select as a reference portion a repetitive portion having a highest sum of respective cross-correlation results.
 14. A device for noise reduction in a signal according to claim 1, further comprising a signal extractor for extracting said repetitive portion.
 15. A device for noise reduction in a signal according to claim 14 comprising an RMS computation unit for calculation of the energy level of segments of wave obtained by averaging a series of said repetitive portions.
 16. A device for noise reduction in a signal according to claim 15, further comprising an RMS value analysis unit for detecting a falloff in said RMS energy value over succeeding averages.
 17. A device for noise reduction in a signal according to claim 14, comprising a cross-correlation unit for computing the cross correlation coefficient of an average of a series of said repetitive portions and a reference wave.
 18. A device for noise reduction in a signal according to claim 17, further comprising a cross-correlation value analysis unit for detecting a falloff in said cross-correlation value over succeeding averages.
 19. A device for noise reduction in a signal according to claim 3, wherein said aligner further comprises a cross-correlator for cross-correlating a current input with said ruing average at a plurality of successive alignments and for aligning said signal on the basis of an alignment giving a maximum cross-correlation.
 20. A device for noise reduction in a signal according to claim 19 wherein said aligner further comprises: an interpolator for interpolating between said cross-correlations at said successive alignments to determine a higher accuracy sub-sample alignment, and a wave shifter for shifting said current input in accordance with said determined sub-sample alignment.
 21. A device for reducing noise in signals having successive substantially repetitive portions, comprising: an aligner for aligning at least some of said substantially repetitive portions one with another, a repetitive portion selector for selecting repetitive portions for passing to said iterative averager on the basis of a comparison with a reference portion, and an iterative averager operative to superimpose and average said aligned, selected portions to produce a running average thereof.
 22. A device for reducing noise in signals according to claim 21, further comprising a repetitive portion selector for selecting repetitive portions for passing to said iterative averager, the repetitive portion selector comprising a reference portion store for storing a reference portion, a cross correlator for computing the cross correlation between a current repetitive portion and said reference portion, and a comparator for comparing a result of said cross-correlation with a predetermined threshold to produce a comparison output, and wherein said selector is operable to pass said current repetitive portion to said iterative averager in accordance with said comparison output.
 23. A device for reducing noise in signals according to claim 21, further comprising a reference portion determination unit associated with said repetitive portion selector, operable to determine as a reference portion any one of a group comprising a first repetitive portion of a current length of said signal, a final result of a running average of a previous set of iterations and a prior determined typical wave.
 24. A device for reducing noise in signals according to claim 22, wherein said reference portion determination unit is operable to dynamically change between members of said group over a course of a set of iterations.
 25. A device for reducing noise in signals according to claim 22, wherein said reference portion determination unit further comprises a reference portion updater for dynamically updating said reference portion during a course of a set of iterations.
 26. A device for reducing noise in signals according to claim 21, comprising a reference portion determiner, said reference portion determiner comprising, a first store for storing a first set of repetitive portions from said signal, a second store for storing a second set of repetitive portions from said signal, a cross-correlator for cross-correlating repetitive portions from said second set in turn with repetitive portions from said first set to produce a plurality of cross-correlation results for respective repetitive portions in said second set, and a reference selector for selecting one of said repetitive portions in said second set as a reference portion in accordance with its respective cross-correlation results.
 27. A device for noise reduction in a signal according to claim 25, wherein said reference selector comprises a threshold level comparator for comparing each cross-correlation result with a threshold and which is operable to select as said reference portion a repetitive portion having a highest number of respective cross-correlation results exceeding said threshold.
 28. A device for noise reduction according to claim 25, wherein said reference selector comprises a summation unit for summing cross-correlation results of respective repetitive portions and which reference selector is operable to select as a reference portion a repetitive portion having a highest sum of respective cross-correlation results.
 29. A device for noise reduction according to claim 25, wherein said reference selector comprises: a threshold level comparator for comparing each cross-correlation result with a threshold, and a summation unit for summing cross-correlation results of respective repetitive portions exceeding said threshold, and which reference selector is operable to select as a reference portion a repetitive portion having a highest sum of respective cross-correlation results.
 30. A waveform frequency component alignment device for aligning first frequency components of waveforms having first frequency and second frequency components, the device comprising: Band pass filters for extracting respective first and second frequency components of said waveform, a first frequency component aligner for determining a first frequency alignment point of a current waveform with another waveform based on respective first frequency components, and a second frequency aligner for aligning said second frequency components of said respective waveforms based on said first frequency alignment point.
 31. A waveform frequency component alignment device according to claim 30, wherein said other waveform is a running average of preceding waveforms.
 32. A waveform frequency component alignment device according to claim 30, wherein said first frequency component aligner further comprises a cross-correlator for cross-correlating a current waveform with said other waveform at a plurality of successive alignments and for determining said first frequency alignment point on the basis of a one of said successive alignments giving a maximum cross-correlation.
 33. A waveform high frequency component alignment device according to claim 32, wherein said first frequency component aligner further comprises an interpolator for interpolating between said cross-correlations at said successive alignments to determine a sub-sample accuracy alignment point between said successive alignments.
 34. A device for analyzing high frequency components of ECG signals, comprising a data extractor for extracting said high frequency components and a data analyzer for determining, from a change over time in at least a part of said high frequency component, whether said ECG signal contains an indication of the presence of ischemia.
 35. A device for analyzing high frequency components of ECG signals according to claim 34, wherein said at least a part of said ECG signal is a QRS complex.
 36. A device according to claim 35, wherein said change over time is a fall in the energy level of succeeding QRS complexes.
 37. A device according to claim 35, wherein said change over time is a fall in a cross-correlation value of succeeding QRS complexes.
 38. A device according to claim 34, wherein said data extractor comprises a waveform averager for performing iterative averaging over successive ones of said high frequency components to obtain a reduced noise version of said components.
 39. A device according to claim 38, further comprising a selector for passing to said waveform averager only those ones of said successive components which exceed a threshold cross-correlation with a reference component.
 40. A method for reducing noise in signals having successive substantially repetitive portions, comprising: superimposing one by one weightwise in iterative steps weighted instances of at least some of said successive substantially repetitive portions, forming a running average of said portions, determining a noise level in said running average, and ending said iterative steps when said noise level reaches a predetermined level, thereby to produce an average of said substantially repetitive portions having reduced noise.
 41. A method for reducing noise in signals according to claim 40, further comprising a step of aligning at least some of said substantially repetitive portions one with another,
 42. A method for reducing noise in signals according to claim 41, wherein said signal comprises first frequency and second frequency components and said step of aligning comprises substeps of extracting said respective first and second frequency components, using said first frequency components to locate an alignment point in each of successive portions, and using said alignment point to align said second frequency components of each of said successive portions.
 43. A method for reducing noise in signals according to claim 40, wherein said step of ending said iterative steps further comprises ending when said repetitive portions are exhausted.
 44. A method for reducing noise in signals according to claim 40, wherein said step of ending said iterative steps further comprises ending when said running average reaches a preset maximum of included repetitive portions.
 45. A method for reducing noise in signals according to claim 40, further comprising the step of selecting repetitive portions for passing to said iterative averager, the step of repetitive portion selecting comprising substeps of: storing a reference portion, Computing the cross correlation between a current repetitive portion and said reference portion, comparing a result of said cross-correlation with a predetermined threshold to produce a comparison output, and passing said current repetitive portion for iterative averaging in accordance with said comparison output.
 46. A method for reducing noise in signals according to claim 45, further comprising the step of determining as a reference portion any one of a group comprising a first repetitive portion of a current length of said signal, a final result of a running average of a previous set of iterations and a prior determined typical wave.
 47. A method for reducing noise in signals according to claim 46, wherein said step of selecting comprises the further substep of dynamically changing between members of said group over the course of a set of iterations.
 48. A method for reducing noise in signals according to claim 46, wherein said step of selecting further comprises dynamically updating said reference portion during the course of a set of iterations.
 49. A method for reducing noise in signals according to claim 45, comprising a step of determining a reference portion by: storing a first set of repetitive portions from said signal, storing a second set of repetitive portions from said signal, cross-correlating repetitive portions from said second set in turn with repetitive portions from said first set to produce a plurality of cross-correlation results for respective repetitive portions in said second set, and selecting one of said repetitive portions in said second set as a reference portion in accordance with its respective cross-correlation results.
 50. A method for noise reduction in a signal according to claim 49, further comprising: comparing each cross-correlation result with a threshold, and selecting as said reference portion a repetitive portion having a highest number of respective cross-correlation results exceeding said threshold.
 51. A method for noise reduction according to claim 49, wherein said step of determining a reference further comprises: summing cross-correlation results of respective repetitive portions and selecting as a reference portion a repetitive portion having the highest sum of respective cross-correlation results.
 52. A method for noise reduction according to claim 49, wherein said step of determining a reference portion further comprises: comparing each cross-correlation result with a threshold, summing cross-correlation results of respective repetitive portions exceeding said threshold, and selecting as a reference portion a repetitive portion having a highest sum of respective cross-correlation results.
 53. A method for noise reduction in a signal according to claim 40, further comprising the step of extracting QRS complexes from an ECG signal to provide said repetitive portion.
 54. A method for noise reduction in a signal according to claim 40 comprising extracting an RMS energy value from an average of a series of said repetitive portions.
 55. A method for noise reduction in a signal according to claim 54, further comprising a step of analyzing said RMS energy to detect for the presence of a falloff in said RMS energy value over succeeding averages.
 56. A method for noise reduction in a signal according to claim 40, comprising extracting a cross-correlation value from an average of a series of said repetitive portions.
 57. A method for noise reduction in a signal according to claim 56, further comprising the step of analyzing succeeding ones of said cross correlation value to detect the presence of a falloff in said cross-correlation value over succeeding averages.
 58. A method for noise reduction in a signal according to claim 42, wherein said alignment step further comprises cross-correlating a current input with said running average at a plurality of successive alignments, and aligning said signal on the basis of an alignment giving a maximum cross-correlation.
 59. A method for noise reduction in a signal according to claim 58, said step of alignment further comprising interpolating between said cross-correlations at successive alignments to determine a high accuracy alignment between said successive alignments.
 60. A method for reducing noise in signals having successive substantially repetitive portions, comprising: aligning at least some of said substantially repetitive portions one with another, selecting repetitive portions for passing to said iterative averager on the basis of a comparison with a reference portion, and superimposing and averaging said aligned, selected portions to produce a running average thereof.
 61. A method for reducing noise in signals according to claim 60, further comprising a step of selecting from said repetitive portions for passing to said iterative averager, the step of selecting comprising substeps of: storing a reference portion, carrying out a cross correlation between a current repetitive portion and said reference portion, comparing a result of said cross-correlation with a predetermined threshold to produce a comparison output, and passing said current repetitive portion to said iterative averager in accordance with said comparison output.
 62. A method for reducing noise in signals according to claim 60, comprising the further step of selecting as a reference portion any one of a group comprising a first repetitive portion of a current length of said signal, a final result of a running average of a previous set of iterations and a prior determined typical wave.
 63. A method for reducing noise in signals according to claim 62, wherein said step of selecting a reference portion includes a substep of dynamically change between members of said group over the course of a set of iterations.
 64. A device for reducing noise in signals according to claim 62, wherein said step of selecting a reference portion includes dynamically updating said reference portion during the course of a set of iterations.
 65. A method for reducing noise in signals according to claim 61, comprising a step of determining a reference point, said step comprising, storing a first set of repetitive portions from said signal, storing a second set of repetitive portions from said signal, cross-correlating repetitive portions from said second set in turn with repetitive portions from said first set to produce a plurality of cross-correlation results for respective repetitive portions in said second set, and selecting one of said repetitive portions in said second set as a reference portion in accordance with its respective cross-correlation results.
 66. A method for noise reduction in a signal according to claim 65, comprising the further steps of: comparing each cross-correlation result with a threshold, and selecting as said reference portion a repetitive portion having a highest number of respective cross-correlation results exceeding said threshold.
 67. A method for noise reduction according to claim 65, comprising the further steps of summing cross-correlation results of respective repetitive portions, and selecting as a reference portion a repetitive portion having the highest sum of respective cross-correlation results.
 68. A method for noise reduction according to claim 65, comprising the further steps of: comparing each cross-correlation result with a threshold, summing cross-correlation results of respective repetitive portions exceeding said threshold, and selecting as a reference portion a repetitive portion having a highest sum of respective cross-correlation results.
 69. A method of aligning waveforms having first and second frequency components, said second frequency components being more subject to noise than said first frequency components, the method comprising: extracting respective first and second frequency components of said waveform, determining an alignment point of a current waveform with another waveform based on respective first frequency components, and aligning said second frequency components of said respective waveforms based on said alignment point.
 70. A method of aligning waveforms according to claim 69, wherein said other waveform is a running average of preceding waveforms.
 71. A method of aligning waveforms according to claim 69, comprising the further steps of: cross-correlating a current waveform with said other waveform at a plurality of successive alignments, and determining said alignment point on the basis of a one of said successive alignments giving a maximum cross-correlation.
 72. A method of aligning waveforms according to claim 71, comprising the further step of interpolating between said cross-correlations at said successive alignments to obtain a sub-sample alignment point.
 73. A method for analyzing high frequency components of ECG signals, comprising the steps of: extracting said high frequency components and determining, from a change over time in at least a part of said high frequency component, whether said ECG signal contains an indication of the presence of ischemia.
 74. A method for analyzing high frequency components of ECG signals, according to claim 73, wherein said at least a part of said ECG signal is at least part of a QRS complex.
 75. A method for analyzing high frequency components of an ECG signal according to claim 74, wherein said change over time is a fall in an RMS energy level of succeeding QRS complexes.
 76. A method for analyzing high frequency components of an ECG signal according to claim 74, wherein said change over time is a fall in a cross-correlation value of succeeding QRS complexes.
 77. A method for analyzing high frequency components of an ECG signal according to claim 75, comprising the further step of performing iterative averaging over successive ones of said high frequency components to obtain a reduced noise version of said components.
 78. A method for analyzing high frequency components of an ECG signal according to claim 77, further comprising a selection step of comparing successive waveforms with a reference component and selecting only those ones of said successive waveforms which exceed a threshold cross-correlation level with said reference component for said step of iterative averaging.
 79. A method of obtaining an indication of ischemia in a patient using an ECG signal therefrom, the method comprising: extracting an ECG signal over a duration, extracting from said ECG signal a series of at least partial QRS complexes over said duration, extracting high frequency components of said QRS complexes, analyzing said high frequency components over said duration for at least one of a predetermined quality, and inferring from said predetermined quality an indication of ischemia.
 80. A method according to claim 79, wherein said predetermined quality is a falloff in a cross-correlation level with a reference component.
 81. A method according to claim 79, wherein said predetermined quality is a falloff in the energy level of said component.
 82. A method according to claim 79, wherein said step of extracting said high frequency components comprises carrying out iterative steps of averaging over preselected ones of successive components to reduce noise
 83. A method according to claim 79, wherein said step of extracting an ECG signal is carried out over a duration of a stress test comprising placing the patient in at least one of a group of phases comprising rest, stress, and recovery from stress.
 84. A method according to claim 79, wherein said step of extracting an ECG signal is carried out over a duration of an event being any one of a group comprising: acute myocardial ischemia, other forms of heart failure, coronary occlusion, and coronary angioplasty, said duration being any one of a group comprising before, during and after said event.
 85. A method according to claim 79, wherein said ECG signal is masked by another ECG signal.
 86. A method of producing a noise reduced waveform from a series of substantially similar repeated waveforms having superimposed noise, the method comprising: selecting waveforms having a highest cross-correlation with a preselected reference waveform, and carrying out iterative averaging steps using said selected waveforms.
 87. A method according to claim 83 comprising the further step of ending said iterative averaging when a signal to noise ratio of a result of said iterative averaging has a level below a predetermined threshold. 